Mobile scanning of buildings has become the standard for capturing a 3D scan of an indoor environment. Mobile scanners typically employ various sensor arrays in order to capture the whole environment. In some examples, multiple lasers may be each be oriented in different directions that overlap sufficiently to provide a full coverage or near full coverage scan. These scans generate millions of points along a path traversed by a user. These millions of points are collectively known as a point cloud.
Several issues may arise with respect to mobile scans. For example, a mobile user may not travel at precisely a given speed. As such, scans may be alternately more or less dense due to speed variations. In another example, noise from reflective surfaces or from electronic interference may resulting in false or inaccurate points in the point cloud. Further, ghosting may occur that generates false surfaces in the point cloud. Each of these issues individually or collectively may result in an inaccurate representation of the environment. Complicating these issues is that the sheer volume of points being processed in the point cloud may lead to processing inefficiencies due to limitations in computing power.
As such, methods for point cloud cleaning are provided herein.